BR112019021080A2 - SYSTEM FOR EVALUATING AN UNDERGROUND EARTH FORMATION AND METHODS OF EVALUATING AN UNDERGROUND EARTH FORMATION AND DIRECTING A DRILL DRILL IN AN UNDERGROUND FORMATION. - Google Patents

Abstract

a system and method for assessing an underground earth formation, as well as a method of directing a drill bit into an underground earth formation. The system comprises a profiling tool that is operable to measure formation data and can be located in a borehole intersecting the formation of underground earth. the system also comprises a processor that is in communication with the profiling tool. the processor is operable to calculate multiple solutions (dtbb) using the measured training data, identify dtbb solutions that satisfy a threshold, convert the identified solutions into pixelated solutions by dividing the identified solutions into pixels, generate a model formation based on the pixelated solutions, and evaluate training using the training model generated.

Description

SYSTEM FOR EVALUATING AN UNDERGROUND EARTH FORMATION, AND, METHOD OF EVALUATING AN UNDERGROUND EARTH FORMATION

FUNDAMENTALS

[001] This section is intended to provide relevant background information to facilitate a better understanding of the various aspects of the described modalities. Thus, it must be understood that these statements will be read in this light and not as state of the art admissions.

[002] Drilling and oil production operations require a large amount of information related to the parameters and the bottom conditions. Such information may include the location and orientation of the borehole and drilling set, land forming properties and parameters of the downhole drilling environment. The collection of information regarding formation properties and downhole conditions is commonly referred to as "profiling" and can be performed during the drilling process itself (hence the term "profiling during drilling" or "LWD," frequently used interchangeably with the term “measurement during drilling” or “MWD”).

[003] Various measurement tools are used in measurement applications

LWD. One of these tools is the resistivity tool, which includes one or more antennas to receive a formation response and can include one or more antennas to transmit an electromagnetic signal for the formation. When operated at low frequencies, the resistivity tool can be called an "induction" tool, and at high frequencies, it can be the resistivity tool can be called an electromagnetic wave propagation tool. Although the physical phenomena that dominate measurement can vary frequently, the principles

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2/24 operation for the tool is consistent. In some cases, the amplitude and / or the phase of the received signals is compared to the amplitude and / or the phase of the transmission signals to measure the formation resistivity. In other cases, the amplitude and / or phase of multiple receiving signals are compared to each other to measure the resistivity of formation.

[004] An inversion based on layered models has been used in the electromagnetic resistivity profiling tools to identify the main limits between different forming resistors. The assumption of unidimensional formation (ID) is typically also used in inversion, where each limit is parallel to one another. The formation assumptions are quite true due to the detection range of the measurements used in the inversion. In general, the typical detection range of conventional resistivity profiling tools is about 5 (1.5 meters) to 10 feet (3 meters) and the maximum detection is about 18 feet (5.5 meters).

[005] Ultra-deep resistivity profiling tools detect formation limits at 100 feet (30.5 meters) from the tool, which provides a much deeper detection range than conventional profiling tools. Within the depth of the investigation, there are usually several layers and a qualitative method, such as correlation failures, due to the complexity. A boundary mapping algorithm, such as a distance boundary inversion algorithm for bed (DTBB) is used to interpret the tool's responses qualitatively and assess underground formation to identify formation zones that are suitable for fluid production training, such as hydrocarbons.

BRIEF DESCRIPTION OF THE DRAWINGS

[006] The modalities of the invention are described with reference to the following figures. The same numbers are used in all figures to

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3/24 refer to similar characteristics and components. The features described in the figures are not necessarily shown to scale. Certain features of the modalities may be shown exaggeratedly in terms of scale or somewhat schematically and some details of elements may not be shown for the sake of clarity and conciseness.

[007] FIG. 1 represents an elevation view of a profiling environment during drilling, according to one or more modalities;

FIG. 2 represents a schematic view of a resistivity profiling tool, according to one or more modalities;

FIG. 3 represents a diagrammatic view of a pixelization method, according to one or more modalities;

FIG. 4 represents a graphical view of a training model, according to one or more modalities;

FIG. 5 represents a graphical view of a resistivity profile, according to one or more modalities;

FIG. 6 represents a graphical view of the uncertainty of the resistivity profile of FIG. 5, according to one or more modalities;

FIG. 7 represents a graph view of the resistivity uncertainty as a contour graph, according to one or more modalities;

FIG. 8 represents a graphical view of a formation limit uncertainty profile, according to one or more modalities; and

FIG. 9 represents a graph view of the smoothed uncertainty profile of FIG. 8, according to one or more modalities.

DETAILED DESCRIPTION

[008] FIG. 1 shows one or more modalities of an environment of

Illustrative LWD. A drilling rig 2 supports a tower 4 having a catarina 6 for raising and lowering a drilling column 8. A top drive 10 supports and rotates the drilling column 8 as it is lowered

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4/24 through the wellhead 12. A drill bit 14 is driven by a downhole motor and / or by rotating the drill column 8. When the drill bit 14 spins, it creates a well hole 16 that passes through various formations. A pump 18 circulates drilling fluid 20 through a feed tube 22 through the interior of the drilling column 8 to the drill bit 14. The fluid exits through the holes in the drill bit 14 and flows upwards through the annular in I take the drilling column 8 to transport drilling fragments and cuttings to the surface, where the fluid is filtered and recirculated.

[009] Drill bit 14 is just a piece of bottom composition 24 that includes a mud motor and one or more “drill collars” (thick-walled steel tubing) that provide the weight and rigidity to assist the drilling process. Some of these commands include built-in profiling instruments to collect measurements of various drilling parameters, such as location, orientation, drill weight, borehole diameter, etc. The orientation of the tool can be specified in terms of an angle of the tool face (rotational orientation), an angle of inclination (the slope); and needle direction, each of which can be derived from measurements by magnetometers, tilt meters and / or accelerometers, although other types of sensors such as gyroscopes may alternatively be used. In a specific embodiment, the tool includes a 3-axis fluxgate magnetometer and a three-axis accelerometer. As is known in the art, the combination of these two sensor systems allows measurement of the tool's face angle, tilt angle and needle direction. Such orientation measurements can be combined with gyroscopic or inertial measurements to accurately track the position of the tool.

[0010] The background composition 24 may include a device for measuring formation resistivity, a gamma ray device for measuring the

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5/24 intensity of gamma ray formation, devices for measuring the inclination and azimuth of the drilling column 8, pressure sensors for measuring well hole pressure, temperature sensors for measuring well hole temperature, etc. Also included in the background composition 24 is a telemetry sub that maintains a communications link with the surface. Mud pulse telemetry is a common telemetry technique for transferring tool measurements to surface receivers and receiving surface commands, but other telemetry techniques can also be used. For some techniques (e.g., acoustic signaling through the wall) the drill string 8 includes one or more repeaters 30 to detect, amplify and retransmit the signal. On the surface, transducers 28 convert signals between mechanical and electrical forms, allowing a network interface module 36 to receive the signal link from the sub telemetry and (at least in some modalities) transmit a downlink signal to the telemetry sub.

[0011] A computer system 50 located on the surface receives a digital telemetry signal, signal receiver and displays tool data or well profiles to a user. Although FIG. 1 illustrating computer system 50 as being connected to the telemetry system, it should be appreciated that computer system 50 may be in wireless communication with the telemetry system. Computer system 50 may include a processor and a non-transitory machine-readable medium 53 (for example, ROM, EPROM, EEPROM, flash memory, RAM, a hard disk, a solid state disk, an optical disk, or a combination of the able to execute instructions. The processor of the computer system 50 can include one or more processors located on the surface or in the well bore, as integrated with the bottom composition 24. The software (represented in Fig. 1 as the machine-readable non-transitory medium 52) controls the functioning of the system 50. A user interacts with the system

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6/24 and software 52, via one or more input devices 54 and 55 and one or more output devices 56. In some embodiments of the system, a driller employs system 50 to make geo-orientation decisions (for example, modifying the path of the well hole or directing the drill 14) and communicating commands appropriate to the bottom composition 24 to execute those decisions. Computer system 50 is operable to perform calculations or operations to assess formation, identify formation limit positions and / or direct drill bit 14 as described here.

[0012] The drill column shown in FIG. 1 illustrates an operation, where drilling is carried out along a path other than a straight down vertical path. At least in some illustrative modalities, the change of direction is made using a “curved sub,” which is a tubular section along the perforation near the bent or curved drill bit. The curve or bend can be fixed or variable, with the direction of the perforation being determined through the curve alone, or by a combination of the bend and the rotation of the perforation. For example, if a downhole motor is used to guide the drill bit and drilling with a fixed curve sub is maintained in a fixed azimuth orientation, the drill column will gradually change direction in the direction of the curve. If, instead, such a perforation is rotated, the perforation will progress along a line parallel to the perforation section above the curve and around the drilling bit processes.

[0013] For drill columns capable of varying the angle of the curved sub, the sub is defined as a desired angle and direction while the drill column is maintained in a desired fixed azimuth orientation, with the drill being driven by the bottom engine of the well. This is sometimes called “sliding drilling”, as the drill column slides through the well hole without turning. In other drilling modalities, drilling

Petition 870190100458, of 10/07/2019, p. 14/52 / 24 continues to be rotated and the angle of the curved sub is maintained, applying a force on the drill string in a specific direction. This causes the sub to be pushed into the well hole wall opposite the desired drilling direction to create an angle between the drill column tubes and / or the bottom composition units on each side of the sub. Such systems are sometimes referred to as swiveling swivel systems. [0014] Due to the angle change introduced by the subs and systems described above used in directional drilling and due to the folds produced in the drilling column by the resulting well hole, the profiling tool subs located along the length of the drilling column can be oriented in different directions. This is particularly true for profiling tools used in deep formation assessment (ie tools in which a transmitter antenna is separated from a receiving antenna at least 20 feet), as the transmitting and receiving antennas used in such tools can be. housed in the subs of the profiling tool that are separated by greater distances (compared to other profiling tools) to achieve the desired formation penetration of the transmitted signals. The greater the distance between the profiling tool subs, the greater the difference in the angle of inclination and stroke can be between sections of the drill column crossing a well hole path that is curved or not in a straight line. As used herein, the angle of inclination of an LWD tool sub that houses an antenna is defined as the angle between a vertical axis z and a local axis of the drill column z for said antenna. The strike angle is defined as the angle between a normal reference vector for a vertical z axis and a projection on a horizontal x-y plane of the local drilling column of the antenna's z axis.

[0015] FIG. 2 shows a schematic view of a tool for

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8/24 profiling of deep resistivity 200, according to one or more modalities. The resistivity profiling tool 200 can be included with the bottom composition 24 of FIG. 1 and includes two LWD 202 and 206 tool subs in different locations and orientations along a drill string. A receiving antenna of the resistivity profiling tool 212 and a corresponding receiving antenna position measuring device 222a is housed in the sub tool of LWD 202, while a transmitting antenna of the resistivity profiling tool 216 and a position measuring device corresponding transmitting antenna 222b (components of an instrument “in the drill”) are housed in the tool sub of LWD 206 and closer to the drill bit 208. Position measuring devices 222a, b locate the position of each corresponding antenna, which can be expressed, for example, in terms of the angle of inclination of each antenna ('and in z _r z _{t in} relation to the axes and respectively; generally fixed and known), the azimuth angle of each antenna (and in relation to the x axis ), each angle

$. _r Φέ of inclination of the sub of the LWD tool (e), and the distance d 'between the antennas. Various methods can be used to locate the positions of the antenna (for example, in relation to a reference position on the surface. It should be noted that, although the angles of the curved sub are typically less than five degrees, the illustration shown has very different angles. more pronounced to better illustrate the effect of angles on the relative spatial locations of the antennas, described in more detail below.

[0016] The resistivity profiling tool 200 in communication with the computer system 50 of FIG. 1 is used to measure formation data, which is used to assess formation and / or determine a well bore path to produce formation fluids, such as hydrocarbon fluids. It should be appreciated that the resistivity profiling tool 200 is an exemplary tool for measuring

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9/24 training data and what other suitable profiling tools can be used. In addition, other resistivity profiling tools may employ different antenna configurations to assess formation. [0017] The measured training data can be used to generate a resistivity model of the formation and determine the uncertainty of a parameter included or determined from the training data. A resistivity model can be used to identify limit positions between formation layers and determine the path of the well hole to produce formation fluids. The uncertainty of a parameter indicates a range of values suitable for a specific parameter, such as the uncertainty of the resistivity values or the limit positions of the formation layers. For example, the uncertainty of a limit position provides an indication of where a formation limit can be located and to what extent that formation limit position can vary. As used in this document, the uncertainty of a parameter refers to a range of suitable values for the parameter or a measurement that is used to quantify a variation in the parameter (for example, standard deviation). The parameter can include any or any combination of horizontal resistivity, vertical resistivity, conductivity, an anisotropy ratio, a limit position of the formation layers and an immersion in the formation.

[0018] FIG. 3 shows a flow chart of a method 300 for generating a training model using training data measured from the resistivity profiling tool 200 of FIG. 2, according to one or more modalities. In block 302, training data is used to generate DTBB solutions using a DTBB inversion method. In block 304, the DTBB solutions are filtered to produce the DTBB solutions that best fit the measured training data. In block 306, the filtered solutions are converted to pixelated solutions, as described herein. Pixelated solutions provide an efficient way to analyze solutions

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DTBB representing varying numbers of training layers. In block 308, an average of the pixelated solutions can be used to generate a training model summarizing the DTBB solutions. In block 310, the formation model can be used to assess formation, develop a well hole path or direct a drill and drill to produce formation fluids.

[0019] In block 302, to generate a resistivity model of the formation, several estimates are applied to a DTBB inversion method to provide multiple DTBB solutions that best fit the measured formation data. DTBB inversions are performed with multiple initial random estimates with one or more layers of formation. In block 304, after inversion, DTBB solutions, which can include several hundred solutions, are identified by the extent of the mismatch with the measured training data. DTBB solutions that satisfy a threshold (for example, a minimal residual solution) can be identified for further processing. The DTBB solutions that fit best with formation measurements are selected as the final solutions. The inversion and DTBB solution selection process is done repeatedly at each profiling point or measuring depth of the resistivity profiling tool.

[0020] In block 306, as soon as the solutions are identified, the solutions are converted into pixelated solutions, which divide the solutions into pixels representing one or more values of formation parameters in a true vertical depth (TVD) or a range of TVDs depending on the pixel width. As an example, FIG. 4 shows a diagrammatic view of the conversion of an inversion solution 302, which comprises three different formation layers (404, 406 and 408), to a pixelated solution 410, according to one or more modalities. The 402 inversion solution is a 3-layer solution that provides three layers of

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11/24 different training. Each identified solution can be divided into pixels, including formation parameter information (for example, horizontal resistivity, vertical resistivity, conductivity, anisotropy ratio and formation immersion) depending on the true vertical depth. Each pixel can have a constant width along the TVD direction and thus a pixel represents one or more parameters of formation in a given TVD or a TVD band depending on the width of the pixel along the TVD direction. A weighted function 412 can also be applied to the pixels to adjust the values of certain pixels and to compensate for any uncertainties in the pixelated solution. For example, pixels away from the tool's position, which are less sensitive to resistivity limits, can be adjusted to compensate for inaccuracies. Once converted to a pixelated solution, the limit positions are implicitly represented by pixels located at the limits of resistivity contrasts 414 and 416. The pixelated solutions thus provide an efficient way to analyze DTBB solutions with different numbers of layers, as described here . Alternatively, a statistical analysis, such as P5, P50 or P95, can also be applied to pixels to generate the final pixelated inversion solution.

[0021] The pixelated solutions can be summarized using a model average of the pixels at each measurement depth and a TVD or a range of TVDs. Therefore, in block 308, a model averaging scheme can be used to generate a model for forming pixelated DTBB solutions. A mathematical medium, including algebraic, geometric or harmonic mean:

can be applied to the selected solutions to calculate a formation model, where is the average value and ^E are the pixels of the pixelated solutions for a specific measurement depth and a TVD

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12/24 or range of TVDs depending on the pixel width.

[0022] FIG. 5 shows a graphical view of a training model 500 generated using synthetic DTBB training data, according to one or more modalities. Formation model 500 is the average of the harmonic model of the pixelated solutions for each measurement depth. The 500 training model condenses hundreds of inversion solutions for each measurement depth into a single graph and provides an efficient data set for evaluating the formation. As shown, the graph is a contour plot of horizontal resistivity (ohm-m) as a function of the measurement depth (x-axis) in relation to the true vertical depth (y-axis). Each measurement depth is the harmonic mean of pixelated solutions. In block 310, a path of well bore 502 can be developed to penetrate one or more formation layers suitable for the production of formation fluids, such as hydrocarbons. The resistivity contrasts (404 and 406) demonstrate formation limit positions between formation layers comprising different formation properties, such as shale, limestone, sandstone, gaseous sandstone, oily limestone, etc. The limit positions of the formation layers identified in formation model 400 can be used to direct the drill bit to a suitable formation layer or to identify the path of the well hole 402 to penetrate one or more formation layers for production, such as training layers suitable for the production of training fluids. For example, the trajectory of well bore 402 can be identified to advance predominantly between the limit formation positions identified based on resistivity contrasts 404 and 406.

[0023] The various mathematical means provide different interpretations of the training model. For example, the algebraic average directly averages the resistivity in ohm-m. The geometric mean calculates the mean on a logarithmic resistivity scale. The means

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13/24 algebraic and geometric are mathematical means that can highlight models with high resistivity values. The harmonic mean calculates the mean conductivity and then converts the mean conductivity into resistivity. For an induction-based resistivity LWD tool, harmonic averaging is generally used because the tool is sensitive to conductivity rather than resistivity. Therefore, pixels with high conductivity can be highlighted when averaging from the measurements of an induction profiling tool. The differences between the average values calculated with the various mathematical means can also represent a parameter uncertainty.

[0024] The model average can also be calculated by not including outer layer pixels among the pixelated solutions in the calculation of the average. One or more pixels from a pixelated solution (for example, a pixel set or an entire pixelated solution) can be removed from the solution set before averaging, if the pixels are considered outliers. As used in this document, an outlier pixel can be a pixel that does not satisfy a threshold value or condition. It must be considered that the parameter used to identify external pixels can be any or any combination of formation parameters including horizontal resistivity, vertical resistivity, conductivity, anisotropy ratio and formation immersion.

[0025] An example of a refined average value for discarding out-of-range pixels is the trimmed average, which calculates the average after discarding parts of a distribution or probability samples (for example, the pixels in a specific DTV and the depth of measurement) at the highest and / or lowest values between pixels. Pixels are sequenced for a specific DTV and measurement depth, and pixels within the highest and lowest values for a given percentage (for example, the highest and lowest 20%) are discarded.

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The highest and lowest percentages can serve as a threshold value for refining pixelated solutions. Only the retained pixels are calculated to define the final solution, for example, a resistivity model of the formation.

[0026] Another example of the refinement process is that the standard deviation can be used as a threshold value to identify the outer layer pixels. The pixels that satisfy the following expression:

/ = 3Q - H | <c S can be used as a condition for discarding discrepancies and averaging, where c is a coefficient (for example, 0.5), S is the standard deviation of pixel solutions for that specific true vertical depth and measurement depth , and I is the set of pixels that satisfy the condition that the absolute difference from the mean (H) of a pixel

Λ () is less than or equal to the weighted standard deviation. Likewise, the absolute difference in the average that satisfies a separate threshold value can also be used as a condition to filter outer layer pixels in the calculation of the model average.

[0027] The standard deviation S can also be used to determine the uncertainty of a parameter included in the final formation model, which is calculated based on the average of the pixelated solutions. The standard deviation S is given by the expression:

where H is the average pixel between pixelated solutions, which can be calculated using various mathematical means (for example, algebraic, geometric or harmonic mean), as previously discussed. As discussed earlier, the uncertainty of a parameter provides an indication

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15/24 of the extent to which the value of a parameter (for example, horizontal resistivity or drop in formation) can vary.

[0028] FIG. 6 shows a graphical view of a resistivity profile 600, according to one or more modalities. The resistivity profile 600 is the average of the pixelated solutions for the horizontal resistivity at a particular measurement depth (20,979 feet / 6394 meters) based on the same synthetic DTBB formation data used in FIG. 5. The resistivity profile 600 is generated by averaging the pixelated solutions for a corresponding TVD or a range of TVDs depending on the pixel width. The resistivity profile 600 is represented as a function of the true vertical depth and can be seen as taking a vertical slice of a particular measurement depth (20. 979 feet / 6394 meters) from the average horizontal resistivity represented in FIG. 5. Similar to FIG. 5, the resistivity profile 600 can be used to identify formation limits in resistivity contrast TVDs 602 and 604.

[0029] FIG. 7 shows a graphical view of the uncertainty of the resistivity profile 700 determined from the pixelated solutions, according to one or more modalities. The uncertainty of the resistivity profile 700 is the standard deviation of the resistivity profile represented in FIG. 6. As shown, the uncertainty profile 700 is a function of the true vertical depth at a particular measurement depth (20,979 feet / 6394 meters) and provides an indication of the range of values suitable for resistivity in a specific DTV or a range of TVDs depending on the pixel width. The uncertainty profile 700 can also be used to identify limit formation positions, which, in turn, facilitates the development of a well hole trajectory or directs the drill bit to a suitable formation layer for fluid production training.

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[0030] FIG. 8 shows a graphical view of the resistivity uncertainty 800, according to one or more modalities. Resistivity uncertainty 800 is the standard deviation of pixelated solutions for horizontal resistivity based on the same synthetic DTBB formation data used in FIG. 5. The standard deviation 700 for the horizontal resistivity is represented as a plot of the horizontal resistivity as a function of the measurement depth (x-axis) in relation to the true vertical depth (y-axis). As shown, a well bore path 802 can be developed to penetrate one or more formation layers suitable for the production of formation fluids, such as hydrocarbons. Limit positions between different layers of formation can be identified using the resistivity uncertainty contour graph 800 by identifying areas of contrast in the resistivity uncertainty (804 and 806). The standard deviation can also be used to determine the uncertainty of other parameters including, but not limited to, vertical resistivity, conductivity, anisotropy ratio and formation immersion.

[0031] As the contour positions are represented implicitly based on the contrast between the resistivity pixels, as represented in FIGS. 5 and 6, the limit positions can also be identified using a first order derivative of the horizontal resistivity:

= £ (¾) δ

where it represents the differential operator. The horizontal resistivity derivative “approaches zero within a formation layer, while the peaks (local minimums or maximums) of the derivative indicate limit positions. A maximum threshold can be used to rule out minor fluctuations in the derivative and inaccurate indications of a limit position. The derivative can also be smoothed before determining the uncertainty of the limit positions to improve the determination of the uncertainty of the

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17/24 limit position. For example, the derivative can be smoothed by applying a filter, including but not limited to a convolution given by the expression:

$ 3.¾ - * si) = - A * u dx dx where is the convolution operator, is the pixel of average horizontal resistivity, is the pixel at a true vertical depth and is a smoothing function. Derivative ⁵ can also be smoothed by applying other suitable smoothing filters, including, but not limited to, a Gaussian filter.

[0032] The uncertainty of a limit position can be identified by the width of a peak found in the derivative. The limit uncertainty includes, but is not limited to, the total width of the peak or a value less than the total width of the peak (for example, half width). If the average horizontal resistivity includes sharp contrasts in resistivity, the limit uncertainty can be relatively small, since the pixelated solutions indicate the same or similar limit positions. On the other hand, if the average horizontal resistivity changes gradually, the limit uncertainty can be large, indicating a large variation in the limit position between the pixelated solutions.

[0033] FIG. 9 shows a graphical view of a limit uncertainty profile 800, according to one or more modalities. The limit uncertainty profile 900 is determined by calculating the first derivative of the resistivity profile 600 represented in FIG. 6. The limit uncertainty profile 900 is represented as a function of the true vertical depth similar to the profiles of FIGS. 6 and 7. Local highs and lows indicate the locations of potential limit positions. Limit positions can be used to develop a borehole path to penetrate suitable formation layers to produce a formation fluid, such as a hydrocarbon.

[0034] FIG. 10 shows a graph view of a profile

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18/24 uncertainty smoothed 1000, according to one or more modalities. The smoothed uncertainty profile 1000 is generated by filtering the boundary uncertainty profile ti using the convolution smoothing function, as discussed earlier. After smoothing, the limit positions can be identified as local maximums and minimums (1002 and 1006) in the smoothed uncertainty profile. The limit position uncertainty can be identified as the 1002 width of each peak or as a value less than the peak width (for example, half peak width). The limit position uncertainty provides a range of DVTs where the limit position may exist. Similar to the contour graph shown in FIG. 8, a boundary uncertainty contour plot can also be formed to assess the formation and identify boundary positions.

[0035] In block 310, the limit positions, which are identified using the various graphs (FIGS. 5-9) or medium pixelated solutions, as previously discussed, can also be used to assess the formation, identify a well hole trajectory and / or directing a drill bit (for example, the drill bit 14 of FIG. 1) through or towards a forming layer suitable for the production of forming fluids. For example, a formation layer suitable for the production of formation fluids can exist between peaks 1002 and 1004 shown in FIG. 10. In addition to the peaks 1002 and 1004 that indicate the location of the limit positions, the uncertainties of the limit position are indicated by the widths of the peaks 1002 and 1004. The widths of the peaks 1002 and 1004 indicate the range of TVDs where the limit positions may also exist . A drill bit can be directed or a well hole path can be developed to pass between the peak widths of peaks 1002 and 1004.

[0036] It should be appreciated that the system and methods described here provide a solution necessarily rooted in deep resistivity profiling tools at the bottom of the well to overcome a

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19/24 problem that arises specifically from the inversion methods used to determine formation properties from the formation data measured using deep resistivity profiling tools. Inversion methods using formation data from deep resistivity profiling tools can provide hundreds of solutions in a single measurement depth of the resistivity profiling tool, raising problems in the evaluation of inversion solutions, such as identifying a training model indicative of properties training and the corresponding uncertainties. The methods and system described here summarize the inversion solutions using an average value of pixelated solutions to evaluate the formation, identify a well hole trajectory and / or direct a drill bit to produce formation fluids.

[0037] In addition to the modalities described above, many examples of specific combinations are part of the scope of the disclosure, some of which are detailed below:

[0038] Example 1: A system for assessing an underground formation, comprising:

a profiling tool operable to measure formation data and located in a well bore intersecting the formation of underground earth; and a processor in communication with the profiling tool and operable to:

calculate multiple distance limit solutions for bed (DTBB) using the measured training data, identify DTBB solutions that satisfy a limit, convert the identified solutions into pixelated solutions by dividing the identified solutions into pixels, generate a formation model based on the pixelated solutions , and

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20/24 evaluate training using the training model generated. [0039] Example 2: The system of example 1, in which the profiling tool comprises an operable resistivity profiling tool to measure the resistivity of the formation.

[0040] Example 3: The system of example 1, in which the processor is still operable, determines the parameter uncertainty based on the pixelated solutions.

[0041] Example 4: The system of example 3, in which the parameter uncertainty is determined based on the standard deviation of the parameter determined from the training model.

[0042] Example 5: The system of example 3, in which the parameter uncertainty is determined based on the derivative of a horizontal resistivity determined from the formation model.

[0043] Example 6: The system of example 3, where the parameter includes any or any combination of a horizontal resistivity, an anisotropy ratio, a limit position of formation layers and a formation immersion.

[0044] Example 7: The system of example 3, in which the parameter uncertainty is determined by applying a filter to the derivative of a horizontal resistivity derived from the formation model.

[0045] Example 8: The system of example 3, in which the parameter uncertainty is determined by applying a filter to a derivative of a horizontal resistivity determined from the formation model and identifying a peak width of the convoluted derivative.

[0046] Example 9: The system of example 1, in which the training model is generated by calculating an average of the pixelated solutions.

[0047] Example 10: The system of example 9, in which the average is calculated by not including outer layer pixels between the pixelated solutions.

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[0048] Example 11: Method for assessing an underground formation, characterized by the fact that it comprises:

measure formation data in a well intersecting underground earth formation using a profiling tool, calculate multiple distance limit solutions for bed (DTBB) using the measured formation data, identify DTBB solutions that satisfy a threshold, convert the identified solutions in pixelated resistivity solutions dividing the solutions identified in pixels, generate a training model based on the pixelated solutions, and evaluate the training using the training model.

[0049] Example 12: The method of example 11, wherein the profiling tool comprises an operable resistivity profiling tool to measure the resistivity of the formation.

[0050] Example 13: The method of example 11 further comprises determining the uncertainty of a parameter based on the training model.

[0051] Example 14: The method of example 13, in which the determination of the parameter uncertainty comprises calculating a standard deviation of the parameter based on the formation model.

[0052] Example 15: The method of example 13, in which the determination of the parameter uncertainty comprises:

calculate a derivative of a horizontal resistivity determined from the formation model;

apply a convolution method to the derivative of horizontal resistivity; and identifying a peak width of the convoluted derivative, where the parameter uncertainty includes the peak width.

[0053] Example 16: The method of example 13, where the parameter

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22/24 includes any or any combination of a horizontal resistivity, an anisotropy ratio, a limit position of formation layers and a formation immersion.

[0054] Example 17: The method of example 11, in which the generation of the formation model comprises calculating an average of the pixelated resistivity solutions.

[0055] Example 18: The method of example 15, in which the average is calculated by not including outer layer pixels between the pixelated solutions of the calculation.

[0056] Example 19: A method of directing a drill bit into an underground formation, comprising:

measure formation data in a well intersecting the formation of the underground earth using a profiling tool located in the well bore, calculate multiple bed distance limit solutions (DTBB) based on the measured formation data, identify DTBB solutions that satisfy a threshold, convert the identified solutions into pixelated solutions by dividing the identified solutions into pixels, generate a formation model based on the pixelated solutions, identify a well hole trajectory based on the formation model and direct a drill bit according to the trajectory of the well hole.

[0057] Example 20: Method of example 19, further comprising:

[0058] determine the uncertainty of a formation parameter based on the formation model by calculating a derivative of a horizontal resistivity derived from the formation model, apply a convolution method to the derivative of the horizontal resistivity, and identify a width of

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23/24 peak of the convoluted derivative, where the parameter uncertainty includes the width of the peak.

[0059] The following discussion is directed to various modalities of this disclosure. The figures in the drawings are not necessarily to scale. Certain features of the modalities may be shown exaggeratedly in terms of scale or in a somewhat schematic manner and some details of conventional elements may not be shown for the sake of clarity and conciseness. Although one or more of these modalities may be preferred, the modalities presented should not be interpreted or used as limiting the scope of the disclosure, including the claims. It will be fully recognized that the different teachings of the modalities discussed can be used separately or in any suitable combination to produce the desired results. In addition, one skilled in the art will understand that the description has wide application and the discussion of any modality is only intended to be an example of that modality and is not intended to suggest that the scope of the disclosure, including the claims, is limited to that modality.

[0060] Certain terms are used throughout the description and claims to refer to particular characteristics or components. As will be understood by one skilled in the art, different people may refer to the same characteristic or component by different names. This document is not intended to distinguish between components or features that differ in name, but not in function, unless specifically indicated. In the discussion and in the claims, the terms "including" and "comprising" are used openly and, therefore, should be interpreted as "including, but not limited to ...". Also, the term “couple” or “couple” is intended to mean an indirect or direct connection. In addition, the terms “axially” and “axially” generally mean the

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24/24 long or parallel to a central axis (for example, central axis of a body or port), while the terms "radial" and "radially" generally mean perpendicular to the central axis. The use of “top,” “bottom,” “above,” “below” and variations of these terms is made for convenience, but does not require any specific guidance from the components.

[0061] The reference throughout this specification to "a modality", "the modality" or similar language means that a particular resource, structure or characteristic described in relation to the modality may be included in at least one modality of the present disclosure. Thus, the appearance of the phrase “in the modality”, “in a modality” and similar expressions, throughout this specification, may, but not necessarily, refer to the same modality.

[0062] Although the present invention has been described in relation to specific details, such details are not intended to be construed as limitations on the scope of the invention, except to the extent that they are included in the appended claims.

Claims (15)

1. System for assessing an underground earth formation, characterized by the fact that it comprises: a profiling tool operable to measure formation data and located in a well bore intersecting the formation of underground earth; and a processor in communication with the profiling tool and operable to: calculate multiple distance limit solutions for bed (DTBB) using the measured training data, identify DTBB solutions that satisfy a limit, convert the identified solutions into pixelated solutions by dividing the identified solutions into pixels, generate a training model based on the pixelated solutions , and evaluate training using the training model generated. 2. System according to claim 1, characterized by the fact that the profiling tool comprises an operable resistivity profiling tool to measure the resistivity of the formation. 3. System according to claim 1, characterized by the fact that the processor is still operable, determines the parameter uncertainty based on the pixelated solutions. 4. System according to claim 3, characterized by the fact that the uncertainty of the parameter is determined based on the standard deviation of the parameter determined from the formation model or is determined based on the derivative of a horizontal resistivity determined from the formation model or is determined by applying a filter to a derivative of a determined horizontal resistivity of the formation model and identifying a peak width of the convoluted derivative. 5. System according to claim 3, characterized by the Petition 870190100458, of 10/07/2019, p. 49/52 2/3 the fact that the parameter includes any or any combination of a horizontal resistivity, an anisotropy ratio, a limit position of formation layers and a formation immersion or is determined by applying a filter to the derivative of a horizontal resistivity derived from training model. 6. System according to claim 1, characterized by the fact that the formation model is generated by calculating an average of the pixelated solutions. 7. System according to claim 6, characterized by the fact that the average is calculated by not including outer layer pixels between the pixelated solutions. 8. Evaluation method of an underground earth formation, characterized by the fact that it comprises: measure formation data in a well intersecting underground earth formation using a profiling tool, calculate multiple distance limit solutions for bed (DTBB) using the measured formation data, identify DTBB solutions that satisfy a threshold, convert the identified solutions in pixelated resistivity solutions dividing the solutions identified in pixels, generate a training model based on the pixelated solutions, and evaluate the training using the training model. 9. Method according to claim 8, characterized by the fact that it also comprises determining the uncertainty of a parameter based on the formation model. 10. Method according to claim 9, characterized by the fact that the determination of the parameter uncertainty comprises calculating a standard deviation of the parameter based on the formation model. 11. Method according to claim 9, characterized Petition 870190100458, of 10/07/2019, p. 50/52 3/3 due to the fact that the determination of the parameter uncertainty comprises: calculate a derivative of a horizontal resistivity determined from the formation model; apply a convolution method to the derivative of horizontal resistivity; and identifying a peak width of the convoluted derivative, where the parameter uncertainty includes the peak width. Method according to claim 9, characterized in that the parameter includes any or any combination of a horizontal resistivity, an anisotropy ratio, a limit position of formation layers and a formation immersion. 13. Method according to claim 8, characterized by the fact that the generation of the formation model comprises calculating an average of the pixelated resistivity solutions and the average is calculated by not including outer layer pixels between the pixelated solutions of the calculation. 14. Method according to claim 8, characterized by the fact that it comprises: identifying a well hole trajectory in an underground earth formation based on the formation model and directing a drill bit according to the hole's trajectory. well. 15. Method according to claim 14, characterized by the fact that it further comprises: determine the uncertainty of a formation parameter based on the formation model by calculating a horizontal resistivity derivative derived from the formation model, apply a convolution method to the horizontal resistivity derivative, and identify a peak width of the convoluted derivative, where the parameter uncertainty includes the peak width.